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![Figure 2: The current view of a part of the center of Warsaw [7].](profile/Jaroslaw-Chudzicki-2/publication/326882702/figure/fig1/AS:689597776396293@1541424330262/The-current-view-of-a-part-of-the-center-of-Warsaw-7_Q320.jpg)
The article presents the current problems of drainage of rain sewage from the urban agglomeration area on the example of Warsaw, the capital of Poland. The size of the precipitation and its diversification, both during and in intensity in a given area is associated primarily with the climatic zone, geographical factors and the degree of urbanizatio...
Context in source publication
Context 1
... rainwater, should also be mentioned at that moment. What is meant, here is the International Frederic Chopin Airport in Warsaw, which at the time of its construction (in the 1920s) was located outside the administrative borders of the city, and currently is located within the administrative borders of the city, few minutes' drive from its center (Fig. 6). ...
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Citations
... The land cover creates a mosaic pattern of built-up and green areas with numerous urbanised zones situated on lower terraces and reclaimed flood plains of the Vistula River. In general, the city is perceived as green, the share of green infrastructure at a level of 25% is considered one of the most valuable assets of Warsaw (Bielecka et al. 2018a) especially in the context of climate change and flood risk (Degórska and Degórski 2017;Chudzicki 2018). ...
Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings’ exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management.
